From 0f7ca1bcd643a1ea467e858a194f196535e61d8d Mon Sep 17 00:00:00 2001 From: reneewillson44 Date: Tue, 3 Jun 2025 20:58:33 +0800 Subject: [PATCH] Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart --- ...tplace And Amazon SageMaker JumpStart.-.md | 93 +++++++++++++++++++ 1 file changed, 93 insertions(+) create mode 100644 DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md diff --git a/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md b/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md new file mode 100644 index 0000000..b87990d --- /dev/null +++ b/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md @@ -0,0 +1,93 @@ +
Today, we are excited to reveal that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](http://recruitmentfromnepal.com)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled variations ranging from 1.5 to 70 billion criteria to construct, experiment, and properly scale your generative [AI](https://lovn1world.com) concepts on AWS.
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In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to release the distilled variations of the designs as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language model (LLM) [established](https://www.youtoonet.com) by DeepSeek [AI](https://gantnews.com) that utilizes support discovering to boost reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. An essential identifying function is its support knowing (RL) action, which was utilized to fine-tune the model's reactions beyond the basic [pre-training](https://103.1.12.176) and tweak process. By including RL, DeepSeek-R1 can adjust better to user feedback and objectives, eventually enhancing both importance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, indicating it's geared up to break down complex queries and reason through them in a detailed way. This assisted reasoning process permits the model to produce more precise, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT capabilities, aiming to [produce structured](https://www.globaltubedaddy.com) reactions while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has actually caught the industry's attention as a flexible text-generation design that can be integrated into different [workflows](https://git.tbaer.de) such as representatives, rational thinking and data analysis jobs.
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DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion criteria, making it possible for efficient inference by routing questions to the most pertinent specialist "clusters." This approach allows the model to specialize in different problem domains while maintaining overall [efficiency](http://orcz.com). DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge circumstances to release the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the [reasoning capabilities](https://4stour.com) of the main R1 model to more efficient architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of [training](https://prantle.com) smaller sized, more efficient models to simulate the habits and [thinking patterns](https://157.56.180.169) of the bigger DeepSeek-R1 design, using it as a teacher model.
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You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend releasing this model with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent damaging material, and examine models against essential security criteria. At the time of composing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create numerous guardrails tailored to different use cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls across your generative [AI](http://slfood.co.kr) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, you require access to an ml.p5e circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, [choose Amazon](http://124.70.58.2093000) SageMaker, and validate you're using ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are deploying. To ask for a limit increase, create a limit increase request and reach out to your account team.
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Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To [Management](http://mirae.jdtsolution.kr) (IAM) approvals to use [Amazon Bedrock](https://dvine.tv) Guardrails. For directions, see Set up [consents](https://wavedream.wiki) to utilize guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to present safeguards, avoid hazardous material, and assess designs against crucial safety requirements. You can implement precaution for [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:Lawerence56N) the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to assess user inputs and design actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.
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The basic flow involves the following steps: First, the system gets an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the model for reasoning. After receiving the design's output, another guardrail check is applied. If the output passes this last check, it's returned as the outcome. However, if either the input or output is intervened by the guardrail, a message is returned indicating the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following areas show reasoning using this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized structure designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:
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1. On the Amazon Bedrock console, pick Model catalog under Foundation designs in the navigation pane. +At the time of composing this post, you can utilize the InvokeModel API to conjure up the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a supplier and choose the DeepSeek-R1 design.
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The model detail page offers necessary details about the design's abilities, pricing structure, and execution guidelines. You can discover detailed use instructions, consisting of sample API calls and code snippets for integration. The [model supports](http://www.amrstudio.cn33000) various text generation tasks, including content production, code generation, and question answering, utilizing its support learning optimization and CoT thinking capabilities. +The page also consists of release options and licensing details to assist you get begun with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, pick Deploy.
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You will be prompted to set up the deployment details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of circumstances, go into a variety of circumstances (between 1-100). +6. For example type, pick your circumstances type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can configure advanced security and facilities settings, consisting of virtual personal cloud (VPC) networking, service function approvals, and file encryption settings. For many use cases, the default settings will work well. However, for production implementations, you might desire to review these settings to align with your company's security and compliance requirements. +7. Choose Deploy to start utilizing the model.
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When the implementation is total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. +8. Choose Open in play area to access an interactive interface where you can explore different triggers and adjust design parameters like temperature and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for ideal outcomes. For example, material for inference.
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This is an outstanding way to explore the [model's reasoning](http://42.192.69.22813000) and text generation abilities before incorporating it into your applications. The play ground provides immediate feedback, helping you comprehend how the design responds to different inputs and letting you tweak your triggers for optimum outcomes.
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You can quickly test the design in the play ground through the UI. However, to invoke the deployed design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run [inference](https://napolifansclub.com) using guardrails with the deployed DeepSeek-R1 endpoint
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The following code example shows how to perform inference using a released DeepSeek-R1 model through [Amazon Bedrock](http://120.79.27.2323000) utilizing the invoke_model and ApplyGuardrail API. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have developed the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up reasoning parameters, and sends out a demand to create text based on a user prompt.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML services that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and deploy them into [production utilizing](https://doum.cn) either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart uses 2 hassle-free approaches: utilizing the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you choose the technique that best fits your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following actions to release DeepSeek-R1 [utilizing SageMaker](https://wikibase.imfd.cl) JumpStart:
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1. On the SageMaker console, choose Studio in the navigation pane. +2. First-time users will be triggered to produce a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The model web browser shows available models, with details like the provider name and design capabilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each design card reveals essential details, consisting of:
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- Model name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if applicable), suggesting that this model can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to conjure up the design
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5. Choose the model card to view the model details page.
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The [model details](https://jobsspecialists.com) page includes the following details:
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- The model name and service provider details. +Deploy button to deploy the design. +About and Notebooks tabs with detailed details
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The About tab consists of important details, such as:
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- Model description. +- License details. +- Technical [requirements](https://www.nas-store.com). +standards
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Before you deploy the model, it's suggested to evaluate the design details and license terms to validate compatibility with your usage case.
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6. Choose Deploy to continue with release.
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7. For Endpoint name, utilize the immediately produced name or produce a customized one. +8. For example type ΒΈ choose a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the number of instances (default: 1). +Selecting appropriate [instance types](https://www.globaltubedaddy.com) and counts is essential for cost and efficiency optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time inference is selected by default. This is optimized for [sustained traffic](https://www.com.listatto.ca) and low latency. +10. Review all setups for precision. For this model, we highly recommend adhering to [SageMaker JumpStart](https://right-fit.co.uk) default settings and making certain that network seclusion remains in place. +11. Choose Deploy to release the design.
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The release process can take several minutes to finish.
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When implementation is complete, your endpoint status will alter to InService. At this point, the design is all set to accept reasoning requests through the endpoint. You can monitor the release development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the implementation is total, you can invoke the model using a SageMaker runtime client and integrate it with your [applications](https://www.hirecybers.com).
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the necessary AWS approvals and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for inference programmatically. The code for releasing the design is provided in the Github here. You can clone the note pad and range from SageMaker Studio.
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You can run additional demands against the predictor:
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Implement guardrails and run reasoning with your SageMaker JumpStart predictor
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Similar to Amazon Bedrock, you can also use the ApplyGuardrail API with your SageMaker JumpStart [predictor](http://121.4.154.1893000). You can produce a guardrail using the Amazon Bedrock console or the API, and implement it as shown in the following code:
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Tidy up
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To avoid unwanted charges, finish the actions in this section to tidy up your resources.
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Delete the Amazon Bedrock [Marketplace](https://flexwork.cafe24.com) deployment
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If you deployed the design using Amazon Bedrock Marketplace, complete the following actions:
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1. On the Amazon Bedrock console, under [Foundation models](http://private.flyautomation.net82) in the navigation pane, choose Marketplace [releases](https://derivsocial.org). +2. In the [Managed deployments](http://gitlab.lvxingqiche.com) section, locate the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're erasing the appropriate release: 1. Endpoint name. +2. Model name. +3. Endpoint status
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Delete the SageMaker JumpStart predictor
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The SageMaker JumpStart model you released will sustain costs if you leave it running. Use the following code to erase the endpoint if you want to stop sustaining charges. For more details, see Delete [Endpoints](http://woorichat.com) and Resources.
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Conclusion
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In this post, we explored how you can access and deploy the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, [SageMaker JumpStart](https://storage.sukazyo.cc) pretrained designs, [Amazon SageMaker](https://centerdb.makorang.com) JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning with Amazon SageMaker JumpStart.
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About the Authors
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Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative [AI](http://drive.ru-drive.com) companies build ingenious solutions utilizing AWS services and sped up calculate. Currently, he is concentrated on establishing techniques for fine-tuning and optimizing the inference performance of big language models. In his leisure time, Vivek takes pleasure in treking, enjoying motion pictures, and attempting various foods.
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Niithiyn Vijeaswaran is a Generative [AI](http://code.chinaeast2.cloudapp.chinacloudapi.cn) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](http://47.93.234.49) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](https://aladin.social) with the [Third-Party Model](https://xhandler.com) Science team at AWS.
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Banu Nagasundaram leads item, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://git.mutouyun.com:3005) hub. She is enthusiastic about developing options that help customers [accelerate](https://theneverendingstory.net) their [AI](https://www.videomixplay.com) journey and unlock business worth.
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